1. Field of the Invention
The invention relates generally to the field of determining subsurface structures from passive seismic signals. More specifically, the invention relates to methods for determining networks of rock formation fractures using passive seismic signals. The fracture network may be used as input to simulations of fluid flow through subsurface reservoirs.
2. Background Art
Reservoir simulation is a process by which fluid flow from one or more permeable subsurface rock formations is estimated with respect to time. Such estimation is important, for example, in determining the economic value of a subsurface hydrocarbon reservoir. The estimation is also useful in determining most profitable places to drill wellbores through such reservoirs, production rates of existing wells and the numbers of such wellbores that will most economically drain useful materials from the reservoir (e.g., oil and gas).
The performance of a subsurface reservoir is related to, among other factors, the spatial distribution of permeability in the reservoir. Methods are known in the art for estimating permeability distribution for “matrix” permeability, that is, permeability resulting from interconnections between the pore spaces of porous rock formations. Another type of permeability that is present in some reservoirs, and has proven more difficult to simulate is so called “fracture” permeability. Fracture permeability is associated with breaks or fractures in the rock formation. Fractures may be caused by a number of different mechanisms, including pumping fluid into the rock formation, withdrawing fluid from the formation, tectonic stress, pore pressure changes related to hydrocarbon generation or changes in the weight distribution of the rock formations (“overburden”) above the reservoir rock formation.
One technique for estimating fracture permeability is to generate a discrete fracture network model. Methods to generate possible fracture distributions generally rely on stochastic approaches that also depend on seismic or structural attributes measured from the reservoir rock by using surface active source seismic data, e.g. amplitude vs. offset (AVO) or anisotropic (e.g. horizontal transverse isotropy—HTI) analyses, combined with fracture orientation and frequency statistics acquired from downhole data sources, e.g., well logs and pressure measurements.
Fractured reservoir models of natural fracture networks provide a basis for generating fluid permeability in reservoir rock related to existing fractures by modeling fracture networks with various distributions of fracture size (surface area of the crack face), aperture (distance between the two sides of the broken rock), and orientation. When fracture orientation measurements are not available from downhole data sources and cannot be interpreted from active surface seismic attributes, fracture orientations have been modeled from structural deformation using assumptions regarding stress and strain at the time of deformation. In general, there are very few measurements that can provide fracture size in a particular reservoir away from the wellbore.
Fracture data from downhole sources, however, are accurate only near the wellbore and fracture data from seismic attributes, while providing a constraint for fracture character at the reservoir scale, is accurate for fracture or fault features that can be resolved in the seismic data, in other words downhole measurements usually provide estimates on a significantly different scale than the reservoir scale and need to be upscaled through some assumptions. Seismic anisotropy attributes which can be interpreted to indicate fractures at the scale of tens of meters (the scale of importance for reservoir simulation flow modeling) are not directly imaged in an active source surface seismic volume and seismic anisotropy is only an indirect measurement of the fractures as it may originate from multiple other phenomena (background stress, unaccounted heterogeneity).
Microseismicity induced by reservoir stimulation of the geothermal field has been used to map fracture density. See, Lees, J. M., 1998, Multiplet analyses at Coso geothermal: Bulletin of The Seismological Society of America, 88, 1127-1143. In the Lees publication, a downhole monitoring array of several geophones was used to locate and invert source mechanisms, which provide estimates of fracture orientation. Density of the located events was then used to constrain the fracture density in a reservoir model.
Source mechanism inversion is described in, Jost and Herman, 1989, Seismological Research Letters, Vol. 60, pp 37-57, and in Aki and Richards, Quantitative Seismology, 1980.
Methods for modeling discrete fracture networks are described by Dershowitz, W., and Herda, H., 1992, Interpretation of fracture spacing and intensity, in Rock Mechanics, J. R. Tillerson and W. R. Wawersik (eds.), Balkema, Rotterdam, p. 757-766, and La Point P. R., Hermanson J., Thorsten E., Dunleavy M., Whitney J. and Eubanks D. 2001. 3-D reservoir and stochastic fracture network modelling for enhanced oil recovery, Circle Ridge Phospohoria/Tensleep Reservoir, Wind River Reservation, Arapaho and Shoshone Tribes, Wyoming: Golder Associates Inc., Report DE-FG26-00BC15190, Dec. 7, 2001, 63 p. Several commercial software packages are available that use these methods to generate fracture models. To do reservoir simulation, the fracture networks are used to calculate flow properties given a particular fracture network configuration. One of many methods for calculating fracture permeability is described in Oda, M. 1985, Permeability Tensor for Discontinuous Rock Masses, Geotechnique Vol. 35, p 483.
The above methods have proven less than satisfactory for use with reservoir simulation. There exists a need for methods for generating models of discrete fracture networks that better account for the source mechanism of stochastically determined fractures.